Implements an automated binning of numeric variables and factors with
respect to a dichotomous target variable.
Two approaches are provided: An implementation of fine and coarse classing that
merges granular classes and levels step by step. And a tree-like approach that
iteratively segments the initial bins via binary splits. Both procedures merge,
respectively split, bins based on similar weight of evidence (WOE) values and
stop via an information value (IV) based criteria.
The package can be used with single variables or an entire data frame. It provides
flexible tools for exploring different binning solutions and for deploying them to
(new) data.